Direct Marketing Performance Modeling Using Genetic Algorithms

References

• Altman E. L., Eisenbeis R. A., Sinkey J.Application of Classification Techniques in Business, Banking and Finance (1981) (JAI Press, Greenwich, CT) Google Scholar
• Brieman L. Stacked regressions. (1994) . Technical report 367, Department of Statistics. University of California, BerkeleyGoogle Scholar
• Breiman L., Friedman J. H., Olshen R., Stone C.Classification and Regression Trees (1984) (Wadsworth and Brooks, Monterey, CA) Google Scholar
• Brachman R. J., Anand T., Fayyad U. M., Piatetsky-Shapiro G., Smyth P., Uthurusamy R. The process of knowledge discovery in databases. Advances in Knowledge Discovery and Data Mining (1996) (MIT Press, Cambridge, MA) Google Scholar
• David Shepard AssociatesThe New Direct Marketing: How to Implement a Profit-Driven Database Marketing Strategy (1995) 2nd ed.(Irwin Publishing, Homewood, IL) Google Scholar
• Handbook of Genetic Algorithms (1991) (Van Nostrand Reinhold, New York) Google Scholar
• Dejong K., Spears W. M., Gordon D. F., Davis L. Using genetic algorithms for concept learning. Machine Learn. (1993) 13:161–188Crossref, Google Scholar
• Efron B., Gong G. A leisurely look at the bootstrap, the jackknife, and cross-validation. Amer. Statistician (1983) 37:36–48Crossref, Google Scholar
• Fonesca C. M., Fleming P. J., Forrest S. Genetic algorithms for multi-objective optimization: Formulation, discussion and generalization. (1993) Proc. Fifth Internat. Conf. Genetic Algorithms(Morgan Kaufmann, San Mateo, CA) 416–423Google Scholar
• Goldberg D. E.Genetic Algorithms in Search Optimization and Machine Learning (1989) (Addison-Wesley, Reading, MA) Google Scholar
• Gordon V. S., Whitley D., Forrest S. Serial and parallel genetic algorithms as function optimizers. (1993) Proc. Fifth Internat. Conf. Genetic AlgorithmsSan Mateo, CA(Morgan Kaufmann)177–182Google Scholar
• Greene D. P., Smith S. F. Competition based induction of decision models from examples. Machine Learn. (1993) 13:220–257Crossref, Google Scholar
• Grefenstette J. J., Segre A. M. Incremental learning of control strategies with genetic algorithms. (1989) Proc. Sixth Internat. Workshop Machine Learn.(Morgan Kaufmann, San Mateo, CA) 340–344Crossref, Google Scholar
• Grefenstette J. J., Ramsey C. L., Schultz A. C. Learning sequential decision rules using simulation models and competition. Machine Learn. (1990) 5:265–278Crossref, Google Scholar
• Han J., Fu Y., Fayyad U. M., Piatetsky-Shapiro G., Smyth P., Uthurusamy R. Attribute-oriented induction in data mining. Advances in Knowledge Discovery and Data Mining (1996) (MIT Press, Cambridge, MA) Google Scholar
• Hand D. J.Discrimination and Classification (1981) (John Wiley and Sons, New York) Google Scholar
• Hays W. L.Statistics for the Social Sciences (1973) (Holt, Reinhart and Winston Publishing, New York) Google Scholar
• King R. D., Henry R., Feng C., Sutherland A., Furukawa K., Michie D., Muggleton S. A comparative study of classification algorithms: Statistical, machine learning and neural network. Machine Intelligence, 13: Machine Intelligence and Inductive Learning (1994) (Clarendon Press, Oxford) Crossref, Google Scholar
• Koehler G. J. Linear discriminant functions determined through genetic search. ORSA J. Comput. (1991) 3:345–357Link, Google Scholar
• Koza J. R.Genetic Programming: On the Programming of Computers by Means of Natural Selection (1993) (MIT Press, Cambridge, MA) Google Scholar
• Massand B., Piatetsky-Shapiro G., Simoudis E., Han J. W., Fayyad U. A comparison of different approaches for maximizing the business payoffs of prediction models. (1996) Proc. Second Internat. Conf. Knowledge Discovery and Data MiningMenlo Park, CA(AAAI Press)195–201Google Scholar
• Michalewicz Z.Genetic Algorithms+Data Structures=Evolution Programs (1994) 2nd ed.(Springer-Verlag, Berlin) Crossref, Google Scholar
• Matheus C. J., Chan P. K., Piatetsky-Shapiro G. Systems for knowledge discovery in databases. IEEE Trans. Knowledge Data Engrg. (1993) 5:903–913Crossref, Google Scholar
• Quinlan J. R., Hanson S., Drastal G., Rivest R. Comparing connectionist and symbolic learning methods. Computational Learning Theory and Symbolic Learning Methods (1993) (MIT Press, Cambridge, MA) Google Scholar
• Radcliffe N. J. The algebra of genetic algorithms. Ann. Maths. Artificial Intelligence (1994) 10:339–384Crossref, Google Scholar
• Radcliffe N. J., Surry P. D., Stender J., Hillerbrand E., Kingdon J. The reproductive plan language RPL2: Motivation, architecture and applications. Genetic Algorithms in Optimization, Simulation and Modeling (1994) (IOS Press, Amsterdam) Google Scholar
• Rumelhart D. E., Hinton G. E., Williams R. J., Rumelhart D. E., McClelland J. L. Learning internal representations by error propagation. Parallel Distributed Processing: Exploration in the Microstructure of Cognition, Volume 1: Foundations (1986) (MIT Press, Cambridge, MA) Google Scholar
• Syswerda G., Schaffer J. D. Uniform crossover in genetic algorithms. (1989) Proc. Third Internat. Conf. Genetic AlgorithmsSan Mateo, CA(Morgan Kaufmann)2–9Google Scholar
• Tukey J. W.Exploratory Data Analysis (1977) (Addison Wesley, Reading, MA) Google Scholar
• Weiss S. M., Kulikowski C. A.Computer Systems that Learn (1991) (Morgan Kaufmann, San Mateo, CA) Google Scholar
• Weiss S. M., Kapouleas I. An empirical comparison of pattern recognition, neural nets, and machine learning classification methods. (1989) Proc. Eleventh Internat. Joint Conf. Artificial IntelligenceSan Mateo, CA(Morgan Kaufmann)688–693Google Scholar
• Wolpert D. Stacked generalization. Neural Networks (1992) 5:241–259Crossref, Google Scholar
• Wright A. H., Rawlins G. E. Genetic algorithms for real parameter optimization. Foundations of Genetic Algorithms (1991) (Morgan Kaufmann, San Mateo, CA) 205–218Crossref, Google Scholar